Strategy and
AI governance to turn intention into execution, without losing the human factor.
I help companies, leaders, and startups in Latin America understand, adopt, and scale AI
with a human-centric approach: literacy, use cases with ROI, and AI Governance as an
operating system.
AI Leader (20+ years) | Responsible AI (Strategy → Production) | $120M+ in value created
Specific problems I solve
People don't understand AI; there is fear or unrealistic expectations.
Design executive and team literacy to align language, boundaries, use cases, and responsibilities.
“We have pilots, but they don't make it to production or don't move KPIs.”
I connect business, data, and operations to prioritize use cases, define metrics, and bring AI to execution with governance.
“There are no rules: legal/ethical risk, shadow AI, inconsistent decisions.”
Implement AI Governance (policies, controls, roles, risk assessment, traceability) as a service.
Who is this consulting for?
• General management, C-level executives, VPs, Heads of Product/Data/Operations.
• Startups that need focus (prioritization) and a governance framework from the outset.
• Organizations that want to move from “exploring AI” to operating it with discipline.
Differentiators
• Business-technology bridge: focus on strategy, operations, and decision-making, not
just models.
• Governance and public policy in Responsible AI: active participation in
research and regulatory/ethical frameworks.
• Actual enablement capacity (training): teaching and mentoring in prompt engineering and adoption.
Services designed for executive decision-making and operational execution.
1. AI Literacy & Enablement
To: leaders and teams who need clarity, a common language, and safe practices.
Includes: executive sessions, role-based training, user guides (copilot mindset), principles
of Responsible AI.
Expected outcome: Faster decisions, less cultural friction, consistent usage.
2. AI strategy and roadmap
To: Organizations with too many ideas and too little prioritization.
Includes: inventory of opportunities, prioritization by value/effort/risk, KPIs, roadmap, and
adoption plan.
Expected outcome: focus, quick wins, and a clear path forward.
3. AI Governance as a Service (framework + operation)
To: Companies that require control, compliance, and traceability without bureaucracy.
Includes: Policies, roles (RACI), usage controls, risk assessment, committee/rituals,
quality and security metrics.
Expected outcome: Scalable adoption, risk reduction, internal trust.
4. Executive advisory
To: C-level / VPs que necesitan criterio
externo para decisiones críticas.
Includes: sesiones 1:1, revisión de
iniciativas, governance, y soporte para
comunicación interna
Signs of impact (measurable + verifiable)
$120M+ in value created through AI initiatives (outcome-oriented personal branding).
Publication: From vision to ROI: how to turn AI stories into measurable results.
Global teaching: Prompt Engineering/Design and mentoring in schools and universities.
Leadership in AI at an airline: Deputy Director of Artificial Intelligence.
Awards: Best Operations Strategy / Best Data Analytics Initiative / Best Automation Initiative.
Responsible AI: Governance and coordination with institutions (Global Council for Responsible AI / CAIDP).
Over $120 million in value created
in initiatives (outcome-oriented personal branding).
Leadership in AI at an airline
Deputy Director of Artificial Intelligence.
Responsible AI
Governance and coordination with institutions (Global Council for Responsible AI / CAIDP).
Responsible AI / CAIDP).
Global teaching
Prompt Engineering/Design and mentoring in schools and universities.
Acknowledgements
Best Operations Strategy / Best Data Analytics Initiative / Best
Automation Initiative.
Publications
From vision to ROI: how to turn AI stories into measurable results.
How do we work?
Step 1 — Assessment (Initial Session)
We align business objectives, context, constraints, risks, and success criteria.
Step 2 — Design (roadmap + minimum viable governance)
We define prioritized use cases, roles, basic policies, and the measurement system.
Step 3 — Controlled pilot (quick win)
We tested a case with real data, quality control, traceability, and operational learning.
Step 4 — Scaling
We industrialize: standard prompts/flows, role-based training, monitoring, continuous improvement, and portfolio expansion.
What backs me up
Working with leadership and cross-functional teams to embed AI in strategy and operations.
Training and mentoring: sustained teaching experience in global AI and transformation programs.
Responsible AI for real Formal responsibilities in governance, collaboration with regulators, and research into emerging regulation.
Professional Spanish and English for working with regional and global teams.
Academic qualifications: MIT (No Code AI & ML), Harvard (CS50AI), Stanford (Generative AI: Technology, Business, and Society), Maestría en Big Data-AI & Omnichannel.
AI does not replace teams: it enhances them when there is clarity, governance, and practice.